用分段残差优化预设轨迹，提升运动规划的效率与安全性。
MoRe-ERL: Learning Motion Residuals using Episodic Reinforcement Learning
- 基于时序强化学习识别需调整轨迹段，保留关键动作
- 采用B样条运动基元生成平滑残差修正，提升动态适应性
- 仿真训练策略可直接部署于真实机器人，实现实时迁移
我们提出MoRe-ERL框架，融合时序强化学习（ERL）与残差学习，将预设参考轨迹精炼为安全、可行且高效的特定任务轨迹。该框架可无缝集成至任意ERL方法和运动生成器中。MoRe-ERL识别需要修改的轨迹片段，同时保留关键任务动作；通过B样条基元生成平滑残差调整，确保在动态任务环境下具备良好适应性与轨迹平滑性。实验表明，残差学习显著优于从零开始训练的ERL方法，在样本效率和任务性能上均表现更优。硬件测试进一步验证了该框架：仿真中训练的策略可直接部署于真实系统，实现极小的仿真到现实差距。
原文摘要 · Abstract (English)
We propose MoRe-ERL, a framework that combines Episodic Reinforcement Learning (ERL) and residual learning, which refines preplanned reference trajectories into safe, feasible, and efficient task-specific trajectories. This framework is general enough to incorporate into arbitrary ERL methods and motion generators seamlessly. MoRe-ERL identifies trajectory segments requiring modification while preserving critical task-related maneuvers. Then it generates smooth residual adjustments using B-Spline-based movement primitives to ensure adaptability to dynamic task contexts and smoothness in trajectory refinement. Experimental results demonstrate that residual learning significantly outperforms training from scratch using ERL methods, achieving superior sample efficiency and task performance. Hardware evaluations further validate the framework, showing that policies trained in simulation can be directly deployed in real-world systems, exhibiting a minimal sim-to-real gap.
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